Motivated by ecological communities to develop tools for multi-dimensional time series
Collage of rodent species from the Portal Project; https://portal.naturecast.org/profiles.html
Background
Background
Motivated by ecological communities to develop tools for multi-dimensional time series
Time series of rodent populations
Background
Motivated by ecological communities to develop tools for multi-dimensional time series
Time series of rodent populations with putative qualitative changes noted
Background
Quantify how ecosystems are responding to stressors
- Climate change
- Invasive species
- Landscape alteration
Phase plane of precipitation and temperature at Portal over time
Background
Need to distinguish
- Stochasticity
- Autocorrelation
- Cyclical dynamics
- Gradual change
- Abrupt shifts
Time series of rodent populations zoomed in to moons 200 to 400
Background
Title of Christensen et al 2018 Ecology Report
Christensen et al. 2018 established methods
Goals for LDATS Package
- Standardize
- Generalize
- Optimize
Screencap of LDATS on CRAN; https://cran.r-project.org/web/packages/LDATS/index.html
Statistical Approach
Two-stage analyses:
- Reduce dimensions
- Analyze time series from [1]
Dimension Reduction
Latent Dirichlet Allocation
\(M\) total documents
\(N\) total words
\(w\) word identity
\(z\) topic identity
\(\theta\) topics-in-documents
\(\alpha\) Dirichlet parameter for \(\theta\)
\(\beta\) terms-in-topics
Plate notation for Latent Dirichlet Allocation
Bookshelf
Dimension Reduction
Latent Dirichlet Allocation
Matrix decomposition representation of LDA
Bookshelf
Dimension Reduction
Latent Dirichlet Allocation
Matrix decomposition representation of LDA with ecological terms
Collage of rodent species from the Portal Project; https://portal.naturecast.org/profiles.html
Dimension Reduction
Latent Dirichlet Allocation
Matrix decomposition representation of LDA with ecological terms and Gamma bolded
Collage of rodent species from the Portal Project; https://portal.naturecast.org/profiles.html
Time Series
Abrupt change points
Fit using parallel tempering MCMC
ptMCMC
LDATS Package
Top-level API target: an “lm-style”
LDA_TS(data, topics = 2, nseeds = 1, formulas = ~1, nchangepoints = 0, timename = "time", weights = TRUE, control = list())
LDATS Package
Under-the-hood
LDATS Package
LDATS v0.2.7
Released on CRAN
LDATS Package
LDATS v0.3.0
In development on GitHub
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A caption
Acknowledgements
Funding
National Science Foundation: DEB-1622425, DGE-1315138, DGE-1842473
Gordon and Betty Moore Foundation: Data-Driven Discovery Initiative Grant GBMF4563
